Cloud tasks scheduling optimization for improving solar energy utilization efficiency in data center power supply
Cloud computing demand has caused high energy consumption and carbon emission pressure while generating data center deployment applications,so the efficient utilization of renewable energy in cloud computing environment is proposed.Aiming at the intermittent non-stationary characteristics of solar energy,which is a specific form of renewable energy,we study the cloud task scheduling method to enhance the energy utilization in data center energy supply.DeepAR,a deep autoregressive model for predicting solar energy production capacity,is constructed to design cloud task scheduling strategies and algorithms by taking advantage of the flexible scheduling characteristics of delay-tolerant tasks and scheduled workloads in the time dimension,and simulation experiments are carried out using real task datasets and solar energy production capacity datasets by applying the GluonTS framework.The results show that the matching between computing load and solar power output is improved,and the utilization of solar power supply in data centers is enhanced.
DeepAR modeltime series predictionsolar energycloud tasksscheduling
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